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# Autogenerated By   : src/main/python/generator/generator.py
# Autogenerated From : scripts/builtin/garch.dml

from typing import Dict, Iterable

from systemds.operator import OperationNode, Matrix, Frame, List, MultiReturn, Scalar
from systemds.utils.consts import VALID_INPUT_TYPES


def garch(X: Matrix,
          kmax: int,
          momentum: float,
          start_stepsize: float,
          end_stepsize: float,
          start_vicinity: float,
          end_vicinity: float,
          sim_seed: int,
          verbose: bool):
    """
     This is a builtin function that implements GARCH(1,1), a statistical model used in analyzing time-series data where the variance
     error is believed to be serially autocorrelated
    
     COMMENTS
     This has some drawbacks: slow convergence of optimization (sort of simulated annealing/gradient descent)
     TODO: use BFGS or BHHH if it is available (this are go to methods)
     TODO: (only then) extend to garch(p,q); otherwise the search space is way too big for the current method
    
    
    
    :param X: The input Matrix to apply Arima on.
    :param kmax: Number of iterations
    :param momentum: Momentum for momentum-gradient descent (set to 0 to deactivate)
    :param start_stepsize: Initial gradient-descent stepsize
    :param end_stepsize: gradient-descent stepsize at end (linear descent)
    :param start_vicinity: proportion of randomness of restart-location for gradient descent at beginning
    :param end_vicinity: same at end (linear decay)
    :param sim_seed: seed for simulation of process on fitted coefficients
    :param verbose: verbosity, comments during fitting
    :return: simulated garch(1,1) process on fitted coefficients
    :return: variances of simulated fitted process
    :return: onstant term of fitted process
    :return: 1-st arch-coefficient of fitted process
    :return: 1-st garch-coefficient of fitted process
    """

    params_dict = {'X': X, 'kmax': kmax, 'momentum': momentum, 'start_stepsize': start_stepsize, 'end_stepsize': end_stepsize, 'start_vicinity': start_vicinity, 'end_vicinity': end_vicinity, 'sim_seed': sim_seed, 'verbose': verbose}
    
    vX_0 = Matrix(X.sds_context, '')
    vX_1 = Matrix(X.sds_context, '')
    vX_2 = Scalar(X.sds_context, '')
    vX_3 = Scalar(X.sds_context, '')
    vX_4 = Scalar(X.sds_context, '')
    output_nodes = [vX_0, vX_1, vX_2, vX_3, vX_4, ]

    op = MultiReturn(X.sds_context, 'garch', output_nodes, named_input_nodes=params_dict)

    vX_0._unnamed_input_nodes = [op]
    vX_1._unnamed_input_nodes = [op]
    vX_2._unnamed_input_nodes = [op]
    vX_3._unnamed_input_nodes = [op]
    vX_4._unnamed_input_nodes = [op]

    return op
